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Experimental design and solid state fermentation: Aholistic approach to improve cultural medium for the

production of fungal secondary metabolitesQuentin Carboué, Magalie Claeys-Bruno, Isabelle Bombarda, Michelle

Sergent, Jérôme Jolain, Sevastianos Roussos

To cite this version:Quentin Carboué, Magalie Claeys-Bruno, Isabelle Bombarda, Michelle Sergent, Jérôme Jolain, et al..Experimental design and solid state fermentation: A holistic approach to improve cultural medium forthe production of fungal secondary metabolites. Chemometrics and Intelligent Laboratory Systems,Elsevier, 2018, 176, pp.101 - 107. �10.1016/j.chemolab.2018.03.011�. �hal-01930229�

Chemometrics and Intelligent Laboratory Systems 176 (2018) 101–107

Contents lists available at ScienceDirect

Chemometrics and Intelligent Laboratory Systems

journal homepage: www.elsevier.com/locate/chemometrics

Experimental design and solid state fermentation: A holistic approach toimprove cultural medium for the production of fungalsecondary metabolites

Quentin Carbou�e a,b,*, Magalie Claeys-Bruno b, Isabelle Bombarda b, Michelle Sergent b,J�erome Jolain b, Sevastianos Roussos b

a Vinovalie, PA les Xansos, 81600 Brens, Franceb Aix Marseille Univ, Univ Avignon, CNRS, IRD, IMBE, Marseille, France

A R T I C L E I N F O

Keywords:Solid-state fermentationExperimental designAspergillus nigerNaphtho-gamma-pyrones

* Corresponding author. Vinovalie, PA les XansosE-mail address: quentin.carboue.qc@gmail.com

https://doi.org/10.1016/j.chemolab.2018.03.011Received 24 October 2017; Received in revised forAvailable online 23 March 20180169-7439/© 2018 Elsevier B.V. All rights reserved

A B S T R A C T

Solid-state fermentation (SSF) is a cultural method that holds tremendous potentials for the production ofnumerous microbial value-added compounds in various industries. As for every other process, experimental de-signs can provide tools to improve the product yields, diminish the production time and thus eventuallydecreasing the cost of the whole process. However, SSF, because of its solid nature, implies some constraintswhich consequently require specific tools to efficiently overcome them. The aim of this study was the improve-ment of the production of antioxidant naphtho-gamma-pyrones produced by Aspergillus niger G131 cultivatedusing SSF. Two experimental designs were presented, a combined design, taking into account two different typesof variables to determine a proper solid medium, and a screening design with mixed-level factors to find soluteswith significant positive effects on the output.

1. Introduction

Solid-state fermentation (SSF) is defined as the fermentationinvolving solids in absence (or near absence) of free water; however,substrate must possess enough moisture to support growth and meta-bolism of microorganisms [1]. The SSF system offers several economicaland practical advantages including high product concentration,improvement of product recovery, simple cultivation equipment, lowerplant operational, potential direct utilization of the crude fermentedproducts and also the possibility to use agro-industrial byproductsinstead of synthetic ingredients as substrate, thereby to cost cutting thebulk of the production [2,3]. These solid byproducts could be either asource of carbon and other nutrients or an inert support material forabsorption of nutrients and biomass anchorage. In this case, supple-mentation is needed in order to provide all necessary compounds for anoptimum growth. Macro and micronutrients that are usually added to themedium include phosphorus, sulfur, potassium, magnesium, calcium,zinc, manganese, copper, iron, cobalt, and iodine [4,5]. Each microor-ganism has its own optimal cultivation conditions and requires specificsubstrates depending on the expected products. Optimization of the

, 81600 Brens, France.(Q. Carbou�e).

m 23 January 2018; Accepted 20

.

medium and cultural conditions is hence essential step to maximize theproduction, especially if the process heads towards industrial scale. MostSSF bioprocess optimization studies use response surface methodology(RSM), which is a powerful statistical and mathematical method fortesting multiple process variables and their interactions simultaneouslywith limited experimental trials compared to “one-factor-at-a-time”approach [6]. However, these optimizations are always achieved on adefined solid medium, selected using a classical one-factor-at-a-timeapproach. Indeed most of these studies use only one substrate as abasal solid medium, comparing effects of each substrate separately;resulting that the best solid substrate is fixed for subsequent experiments.In consequence, most studies investigate with precision the effects ofchemical complementation with soluble compounds and physical pa-rameters such as temperature and humidity but neglecting the definitionof a proper solid medium [2,7–12]. In this study we investigated themedium optimization using a holistic strategy, to improve the productionof naphtho-gamma-pyrones (NγPs) – secondary metabolites showinginteresting antioxidant properties – by Aspergillus niger G131 cultivatedon solid support. First, the solid medium composition was defined usingcombined designs – with mixture and discrete variables – and then the

March 2018

Fig. 1. Chromatogram of an ethanolic extract. The structures of molecules areassociated with the peaks.

Q. Carbou�e et al. Chemometrics and Intelligent Laboratory Systems 176 (2018) 101–107

chemical complementation was made using an asymmetric screeningdesign that answer efficiently to the constraints imposed by SSF.

2. Materials and methods

2.1. Strain

A. niger G131, a non-ochratoxicogenic strain, provided by �Ecolenationale sup�erieure agronomique de Toulouse, France was used for itsability to produce antioxidant NγPs such as fonsecin, fonsecin B andustilaginoidin A.

2.2. Inoculum

The fungal strain was conserved at 4 �C in a 5ml bottle on potatodextrose agar (PDA). The inoculum stock was prepared by propagatingthe fungus in Erlenmeyer flasks containing PDA. The cultures wereincubated at 25 �C for 5–10 days. The inoculum suspension was preparedby adding 0.01% (v/v) Tween 80 and scraping with a magnetic stirrer torecover the conidia. The quantity of conidia was counted using aMalassez cell prior to inoculation of the solid medium.

2.3. Solid-state fermentation

Fungal growth was performed in flask on 5 g (dry mass) of solidsubstrate, at 25 �C in a lab oven, at 66% of initial humidity and during 7days. The initial inoculation rate was 2.107conidias/g dry matter. Eachmodality presented in this study was realized in triplicate.

2.4. Determination of the dry weight of the sample

The results presented in this article are expressed in dry weight. Todetermine the dry weight of a sample, 1 g of moist fermented materialwas put in an oven at 105 �C during 24 h and was reweighted to measurethe percentage of lost water.

2.5. Extraction and analysis of NγPs

To analyze the NγPs produced by the fungus, 1 g of moist fermentedmaterial was taken from each flask and mixed with10ml of analyticalethanol during 1 h. The obtained ethanolic extract was then filteredwith 0.2 μm Millipore filter prior to high performance liquid chroma-tography (HPLC) analysis. The HPLC system (Agilent Technologies©,Santa Clara, USA) was equipped with a diode array detector, a RP18analytical column (150� 4.6mm, 5 μm particle size, Zorbax EclipseXDB, Agilent Technologies©), fitted to a RP18 guard column(10� 4mm, Agilent, Santa Clara, USA) and kept at 30 �C during theanalysis. The crude NγP extracts (40 μL) were injected using an auto-sampler. The mobile phase consisted of acidified water (0.2% glacialacetic acid) (A) and acetonitrile (B). The linear gradient started from30% to 100% of B during 45min, then 100% B for 5min at a flow rateof 1mlmin�1, The NγPs were monitored at 280 nm and the concen-trations obtained from subsequent peaks were calculated using Chem-Station B.02.01 (Agilent Technologies©). For a NγP peak, theconcentration was determined using the commercial standard rubrofu-sarin A (BiovioticaNaturstoffe GmbH, Germany). The results wereexpressed in mg equivalent rubrofusarin/g dry matter based on thecalibration curve obtained from the analysis of standard solutionsranging from 2 to 200mg/l. Rubrofusarin A has been chosen as cali-brant in this study because it is one of the few commercial NγP availableon the market. For a given sample, the response variable, “found NγPscontent” corresponds to the whole production of NγPs, calculated as thetotal of each NγP produced. Fig. 1 shows a chromatogram with the NγPsfound in the extract. Growing on a solid medium composed by vegetalsubstrates, the fungus also releases valuable molecules from the solidmatrix during his hydrolytic activity like trans-p-coumaric acid and

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trans-ethyl-ferulate. For the purpose of simplicity and because of theirnon-negligible participation in the general antioxidant activity of theethanolic extract and the fact that their maximum of UV absorption islike the NγPs, at 280 nm, trans-p-coumaric acid and trans-ethyl-ferulateare treated like NγPs in this study.

2.6. Design of experiments

2.6.1. Determination of the solid medium

� Factors and domain of interest

To determine the optimum composition of the solid-medium for theproduction of NγPs, we studied the behavior of 3 substrates in the for-mula –wheat bran, olive pomace and potato flakes, (each component X1,X2 and X3 varying between 0 and 100%) – and the nature of the solidsupport – vine shoots and pine sawdust. Wheat bran has been selected asa potential protein source, olive pomace as a lipid source and potatoflakes as a starch source. The experimental domain of interest is shownTable 1.

For this modelization, we should have performed experiments in thewhole experimental domain, which represents an infinite number offormulas which is of course unthinkable. Therefore, we have used anempirical mathematical model to represent the phenomenon in thedomain of interest. This model based on four predictor variables wasbuilt as follows:

- The three mixture variables (X1, X2, X3) are described with a reducedcubic model in the component fractions [13].ηm ¼ β1X1þβ2X2þβ3X3þβ12X1X2þβ13X1X3þβ23X2X3þβ123X1X2X3

- The discrete variable (X4) is described using polynomial model (de-gree 1)

Finally, the complete model describing the production of NγPs was amultiplicative particular model (only the degree 2 multiplicative termshave been selected plus the mixture cubic term) in order to take into

Table 3Conditions runs of the combined design in 14 experiments plus 2 test points andtheir values of the response “found NγPs content”.

Run X1 X2 X3 X4 Found NγPs contenta

1 1.00 0.00 0.00 Vine shoot 0.9540.7650.953

2 0.00 1.00 0.00 Vine shoot 0.2070.3450.343

3 0.00 0.00 1.00 Vine shoot 3.9513.6423.549

4 0.50 0.50 0.00 Vine shoot 1.7242.0382.456

5 0.50 0.00 0.50 Vine shoot 3.5023.0723.121

6 0.00 0.50 0.50 Vine shoot 1.0141.2111.504

7 0.33 0.33 0.33 Vine shoot 2.7172.5482.664

8b 0.17 0.17 0.66 Vine shoot 2.9263.4613.160

9 1.00 0.00 0.00 Pine sawdust 0.7820.8880.853

10 0.00 1.00 0.00 Pine sawdust 0.2450.267

Table 2Factors and domain of interest for the chemical complementations.

Variables Level 1 Level 2 Level 3

X1 Sodium nitrate 0 1.5X2 Benzoic acid 0 0.5X3 Ammonium molybdate 0 0.2X4 Manganese sulfate 0 0.2X5 Zinc sulfate 0 0.2X6 Ammonium sulfate 0 1.5X7 Glucose 0 2X8 Potassium phosphate 0 0.5X9 Hydrogen peroxide 0 0.34X10 Copper sulfate 0 0.05 0.2X11 Magnesium chloride 0 0.05 0.2

Table 1Factors and experimental domain of interest for the solid medium definition.� Mathematical modeling

Variables Experimental domain

Mixture variables X1 Wheat bran 0% → 100%X2OIive pomaceX3 Potato flakes

Discrete variable X4 Support nature Vine shoot Pine sawdust

Q. Carbou�e et al. Chemometrics and Intelligent Laboratory Systems 176 (2018) 101–107

account the interactions between the discrete variable and the mixturevariables.

η¼ β1X1þβ2X2þβ3X3þβ12X1X2þβ13X1X3þβ23X2X3þβ123X1X2X3þβ14X1X4þβ24X2X4þβ34X3X4

In order to estimate the mathematical model coefficients, experi-ments were carefully chosen since classical design of experiments couldnot be used.

� Construction of the experimental design

To estimate the coefficients of this model a specific design of exper-iments was built. In a first step, 32 candidate points have been generatedto cover the set of possible experiments. To reduce the number of ex-periments, a D-optimal design was chosen. This design was built usingthe exchange algorithm proposed by Fedorov [14]. In this case, thefunction of variance was chosen as the acceptability criterion. 14 ex-periments have been selected as shown in Fig. 2. In order to validate themodel, 2 test points have been added.

2.6.2. Determination of the chemical complementationA screening study was realized to determine the right optimum

complementation of the previously defined solid medium.

� Factors and domain of interest

The influence of 11 variables on the NγPs production was studied: 9variables with 2 levels and 2 variables with 3 levels (Table 2).

The levels are expressed as a percentage of the initial dry matter.

� Mathematical modeling

In this screening study, the effects of factors were supposed to beadditive. This hypothesis implied that all the interaction effects werenegligible. In our case, mixed-level factors were studied and we postu-lated that the production of NγPs was a linear combination of the effectcoded variables X1A, X2A, …, X11B. The reduced reference state modelused for 9 factors with 2 levels and 2 factors with 3 levels is expressed as

Fig. 2. Selected points to estimate the model coefficients. In blue the candidatepoints, in red the selected points and in yellow the selected test points. (Forinterpretation of the references to color in this figure legend, the reader isreferred to the Web version of this article.)

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following:

η¼β0þ β1AX1A þβ2AX2A þβ3AX3A þ…þβ9AX9A þβ10AX10A þβ10BX10B

þβ11AX11A þ β11BX11B

0.37711 0.00 0.00 1.00 Pine sawdust 3.811

4.2543.614

12 0.50 0.50 0.00 Pine sawdust 2.0101.9491.949

13 0.50 0.00 0.50 Pine sawdust 3.0223.9043.454

14 0.00 0.50 0.50 Pine sawdust 2.0161.7971.726

15 0.33 0.33 0.33 Pine sawdust 3.4122.9763.661

16b 0.66 0.17 0.17 Pine sawdust 2.3372.0152.216

a Found NγPs content in mg equivalent rubrofusarine/g of dry matter.b Test point.

Table 5Validation of the model using the 2 test points.

Experimental response Calculated response Difference

2.926 3.18 �0.253.461 0.283.160 �0.022.337 2.59 �0.252.015 �0.572.216 �0.37

Fig. 4. Effect of the discrete variable X4.

Q. Carbou�e et al. Chemometrics and Intelligent Laboratory Systems 176 (2018) 101–107

β1A,…, β9A represent the value of the factor differential effect, i.e. thevariation of the response when the factor changes from level B taken asreference to level A, β10A and β10B and β11A and β11B represent the vari-ation of the response when the factor changes from level C taken asreference to levels A and B respectively.

� Construction of the experimental design

The model having 14 coefficients, an asymmetric design 2932 with 16experiments was built from the optimal symmetrical design 45//16 usingthe Addelman's correspondence method [15]. This allows the construc-tion of asymmetrical design from symmetrical ones by collapsing col-umns, either by replacing a column by another with fewer levels or byreplacing a single four-levels column by three two-levels columns.

2.7. Statistical analysis

The generation and the data treatment of the designs were performedusing the software Nemrod-W [16].

3. Results and discussion

3.1. Determination of the solid medium

The 14 experiments for the model construction were done and theresults are given in Table 3. Moreover, the observation of the differentreplicates indicates that each experiment presents a low variability basedon the observation of the proximity of the points of the same color(Fig. 3).

The calculated values of the model coefficients are given in Table 4.The model validation was performed using a t-test on the difference

between experimental values and predicted values for the 2 test points(Table 5).

Table 4Calculated model coefficients.

Coefficient Value

β1 0.8245β14 �0.0580β2 0.3050β24 0.0953β3 3.8141β34 0.2303β12 5.6900β13 3.9829β23 �1.9857β123 11.4880

Fig. 3. Repeatability of the experiments for the medium definition. Triplicatesof the same experiment are of same color. (For interpretation of the references tocolor in this figure legend, the reader is referred to the Web version ofthis article.)

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The difference between the calculated and the experimental valuesshow no significant difference using a t-test (α¼ 5%). Thus the model canbe applied for prediction using the 16 experiments.

The effect of X4 has been calculated for X1¼ 33%, X2¼ 33% andX3¼ 33%, it appeared that the nature of the support to carry thefermentation had no significant effect on the response variable; indeedthe found NγPs contents are the same whether vine shoots or pinesawdust were used (Fig. 4).

Fig. 5 shows a three-dimensional diagram (a) and a contour plot of thecalculated response surface (b). The optimum ratio to maximize NγPs

Fig. 5. Effect of the mixture variables on the NγPs production.

Table 6Classical asymmetric screening design 2932 in 16 experiments and values of the response “found NγPs content”, the values of the screened variables are expressed as % ofdry matter.

Run X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 Found NγPs contenta

1 0 0 0 0 0 0 0 0 0 0 0 3.7773.6133.220

2 0 0 0 0.2 0 1.5 2 0 0.34 0.05 0.05 3.6733.918

3 0 0 0 0 0.2 1.5 0 0.5 0.34 0.2 0.2 1.9331.7342.488

4 0 0 0 0.2 0.2 0 2 0.5 0 0 0 3.5702.4663.073

5 1.5 0 0.2 0 0 0 2 0 0.34 0.2 0 2.1112.3773.008

6 1.5 0 0.2 0.2 0 1.5 0 0 0 0 0.2 0.8100.9291.230

7 1.5 0 0.2 0 0.2 1.5 2 0.5 0 0 0.05 1.3960.8591.531

8 1.5 0 0.2 0.2 0.2 0 0 0.5 0.34 0.05 0 2.9781.8611.853

9 0 0.5 0.2 0 0 0 0 0.5 0.34 0 0.05 2.1802.7713.036

10 0 0.5 0.2 0.2 0 1.5 2 0.5 0 0.2 0 4.2313.3672.831

11 0 0.5 0.2 0 0.2 1.5 0 0 0 0.05 0 2.6713.7232.254

12 0 0.5 0.2 0.2 0.2 0 2 0 0.34 0 0.2 3.0432.5472.376

13 1.5 0.5 0 0 0 0 2 0.5 0 0.05 0.2 2.5423.8492.500

14 1.5 0.5 0 0.2 0 1.5 0 0.5 0.34 0 0 1.7821.6951.014

15 1.5 0.5 0 0 0.2 1.5 2 0 0.34 0 0 1.6741.5581.634

16 1.5 0.5 0 0.2 0.2 0 0 0 0 0.2 0.05 2.9512.5653.003

a Found NγPs content in mg equivalent rubrofusarine/g of dry matter.

Fig. 6. Repeatability of the experiments for the medium chemicalcomplementation.

Q. Carbou�e et al. Chemometrics and Intelligent Laboratory Systems 176 (2018) 101–107

production is 84% of potato flakes and up to 16% of wheat bran,considering that olive pomace decreases massively the NγPs obtained.This result indicates that NγPs production is positively affected by thepresence of polymeric sugars in the medium.

In SSF, the medium is an important factor to be taken into account,whether it was used as support of culture or substrate. In fact, as sub-strate, it has to efficiently provide microbial nutritional needs, and assupport of culture, it has to possess favorable physical properties havingconsequence on water availability and allowing initial conidialanchorage, mycelial elongation in space and mass and heat transfers tooccur over time [16]. The function difference and the essential aspect ofeach of these variables support and substrate – the fungus does not growon a support alone – force that the two variables, which are of twodistinct natures, must be used simultaneously, hence the utilization of acombined design. Obviously, when using natural byproducts, thedistinction between support and substrate may be blurry, a solid susb-trate, because of its physical nature, participates as a support in thegeneral texture of the medium. However, in this case, a ligneousbyproduct as vine shoots or pine sawdust – rich in biopolymers recalci-trant to the enzymatic degradation by A. niger – acts mainly as a support:

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the fungus preferentially consuming the more easily metabolizablecompounds. Although the porosity changes during the process, as fungaldevelopment may clog the empty spaces in the medium, the support

Table 7Coefficient estimations of the model.

Coefficient Value

β0 1.299β1A 1.024β2A �0.150β3A 0.336β4A �0.130β5A 0.355β6A 0.606β7A �0.327β8A 0.205β9A 0.247β10A �0.559β10B 0.251β11A 0.432β11B 0.475

Q. Carbou�e et al. Chemometrics and Intelligent Laboratory Systems 176 (2018) 101–107

maintains the physical properties favorable for mass and heat transfers tooccur, allowing for example the gaseous phase inside the interparticlevoid to be regenerated in oxygenwhile it is consumed by the fungus [17].Following the same idea, substrate were selected, for their nutritionalquality more than for their participation in the general texture of themedium. The vine shoots used in this study are previously extracted inethanol to recover the polyphenols they contain. The fact that vine shootsshow no difference with an inert ligneous support like pine sawdustsuggests that they are correctly exhausted in compounds that may inhibitthe fungal growth. It is also a good thing because it offers a way ofvalorization of an overproduced by-product in Southern France. Con-cerning substrate effects, NγPs production is maximized with the

Fig. 7. On the left, the effect of each tested variable, orange color means that the varivariable effect is comprised within the significance limits and is thus no significanreference and in blue and green, the effect on the response of the variation from levereferences to color in this figure legend, the reader is referred to the Web version o

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presence of potato flakes, rich in starch, whereas olive pomace and in asmaller extent, wheat bran, reduce the obtained NγPs content. Consid-ering that wheat bran is richer in carbohydrates than olive pomace andcontains starch, it appears that starch is an important compound tomaximize the NγPs production [18,19]. Cost and availability are themain factors considered to select suitable substrates for SSF. The op-portunity for agro-industrial byproducts of low commercial value to beused as substrates in SSF is an important economic valorization approach[20].

However, in the literature, most of the optimization efforts are madeon chemical complementation. In fact, the use of pure commercialcompounds allows more precise studies to evaluate their effects on thephysiology and metabolism of the fungus, ensures also a high level ofrepeatability and enhances the process consistency; on the other hand,using natural byproducts as cultural medium may increase the experi-mental variability between replicates leading to variations in the processperformance, because of the complex nature of the substrate and thus anincrease inherent disparateness of composition and texture and a higherbatch-to-batch variations [21]. To go beyond this problem and reduce theheterogeneity that may exist between two stocks of the same byproduct,it is recommended to use byproducts from the same industrial sector thatpreferentially have the same standardized pre-treatments. In the study,good repeatability can be observed among because of a good inherenthomogeneity of the chosen substrate. Of course, the use of a single solidmatrix complemented with pure compounds may improve the repeat-ability of experiments, when compared to the use of multiple complexsolid substrates. It is however possible to use a mixture of solid substrateand to have a good repeatability if the previous recommendations con-cerning the origin of the medium are respected.

able has a significant effect on the response variable whereas blue means that thetly different from experimental error. On the right, in red the level B taken asl B to level A and from level B to level C, respectively. (For interpretation of thef this article.)

Q. Carbou�e et al. Chemometrics and Intelligent Laboratory Systems 176 (2018) 101–107

3.2. Determination of the chemical complementation

The 16 experiments have been performed and the results are given inTable 6.

The observation of the different replicates indicates that eachexperiment presents a higher variability than the previous experimentcarried out for the solid medium definition (Fig. 6).

The estimations of the model coefficients using multilinear regressionhave been calculated (Table 7).

Statistical tools to identify active variable effects are used togenerate effect plots (Fig. 7). In these plots, the signification of thecoefficients is calculated from the variance which is represented by thevertical dotted line. The orange effects whether they are positive ornegative, are the statistically significant ones. Seven variables have asignificant effect on the response variable and among these, 2 variableshave a positive effect.

Glucose (X7) is scarcely significant and copper sulfate (X10) has apositive effect at both level when it is present in the medium, althoughits intermediary level seems to maximize more the NγPs production.Concerning this screening of chemical compounds for complementa-tion, the use of an asymmetric matrix allows to study more preciselythe effects of two factors, suspected to be critical in the NγPs pro-duction: copper sulfate and magnesium chloride because those vari-ables vary on 3 levels. Thus, though it is a screening study, it givesfurther information on what level is the more adapted to maximizedthe production. Initially, we started the fungus cultivation with a non-optimized medium made of 50% of vine shoots and 50% of wheat branthat produced 1mg of NγPs rubrofusarin/g dry matter. After two stepsof optimization the secondary metabolites yield has been increasedfourfold. More variables could also be studied to fully optimize thefermentation process, such as for example, physical parameters liketemperature, aeration and water activity which are of critical impor-tance in SSF [22].

4. Conclusion

The porosity between chemometrics and biotechnology helps toimprove biotechnological processes such as SSF. Indeed, the diversity oftools offered by experimental design adapt itself very well to the con-straints tied to the SSF process, themselves resulting from the high di-versity of variable natures. A holistic vision of the optimization process inSSF, thus gathering the variables depending on their nature – whetherthey are solid or soluble compounds or physical parameters – and opti-mizing step by step from the definition of a complex solid medium to theright adapted chemical complementation and/or the right operativeconditions like temperature and aeration can fully express the potentialof the fungus following a simple strategy. In addition, this strategy ofoptimizing the best conditions of the previous design leads inevitably to amaximized result. If the final aim is the industrial scale-up of the pro-duction, it is important however, to define the adapted economic limitregarding the medium ingredients supply and the fermentation operativeparameters.

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Acknowledgements

Support from Vinovalie (CIFRE ANRT N� 2015/0027) is gratefullyacknowledged.

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